Why is cross-validation crucial in assessing the reliability of a battery model's predictions?

Cross-validation is crucial in assessing the reliability of a battery model's predictions because it provides an estimate of how well the model will generalize to unseen data, preventing overfitting and providing a more realistic assessment of model performance. When developing a battery model (e.g., an equivalent circuit model or an electrochemical model), the model's parameters are typically tuned using a dataset of battery voltage, current, and temperature measurements. This process is called 'training' the model. However, if the model is too complex or the training dataset is too small, the model may 'overfit' the data. Overfitting means the model learns the training data too well, including the noise and random variations pres....